The network is trained by optimizing the AAM-softmax [1]} loss on the speaker identities in the training corpus. The AAM-softmax is a powerful enhancement compared to the regular softmax loss in the context of fine-grained classification and verification problems. It directly optimizes the cosine distance between the speaker embeddings. As a consequence, complex scoring backends such as Probabilistic Linear Discriminant Analysis (PLDA) [2]} can be avoided.
